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I filter the 25-frames-per-second STATS/SportVu optical tracking data of 233 regular and post season 2011-2012 NBA games for half-court situations that begin when the last player crosses half-court and end when possession changes, resulting in a universe of more than 30,000 basketball plays, or about 130 per game. To categorize the plays algorithmically, I describe the requirements a suitable dynamic language must have to be both more concise and more precise than standard X’s and O’s chalk diagrams. The language specifies for each player their initial starting spots, trajectories, and timing, with iteration as needed. A key component is acceleration. To determine optimal starting spots, I compute burst locations on the court where players tend to accelerate or decelerate more than usual. Cluster analysis on those burst points compared to all points reveals a difference in which areas of the court see more intense action. The primary burst clusters appear to be the paint, the top of the key, and the extended elbow and wing area. I document the most frequently accelerating players, positions, and teams, as well as the likelihoods of acceleration and co-acceleration during a set play and other components intended to collectively lead to an algorithmic taxonomy.
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